Title: An opposition-based firefly algorithm for medical image contrast enhancement

Authors: Amer Draa; Zeyneb Benayad; Fatima Zahra Djenna

Addresses: MISC Laboratory, Department of Fundamental Informatics and its Applications, Constantine 2 University, Constantine, Algeria ' Department of Fundamental Informatics and its Applications, Constantine 2 University, Constantine, Algeria ' Department of Fundamental Informatics and its Applications, Constantine 2 University, Constantine, Algeria

Abstract: Image enhancement is a crucial pre-processing step in almost every medical imaging system. Different types of degradation can occur in medical images such as noise, blur and contrast imperfection. Filtering techniques have been successfully applied for denoising and deblurring images, while contrast enhancement has been achieved using histogram equalisation. This technique has the main drawbacks of losing details included in minor grey levels or over-enhancement. As a solution, the grey-level mapping technique has been adopted. Defining the new set of grey levels, to substitute those of the input image, using an exhaustive search is computationally complex; so metaheuristics are generally used. In this topic, this paper presents a new opposition-based firefly algorithm to search the best target set of grey levels for medical image contrast enhancement. The obtained results are compared against those obtained by histogram equalisation and classical variants of the firefly algorithm.

Keywords: grey-level mapping; contrast enhancement; firefly algorithm; FFA; opposition-based learning; OBL; medical imaging; image enhancement; image contrast; medical images.

DOI: 10.1504/IJICT.2015.070299

International Journal of Information and Communication Technology, 2015 Vol.7 No.4/5, pp.385 - 405

Received: 24 Jul 2013
Accepted: 06 Sep 2013
Published online: 02 Jul 2015 *

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